Creating a universal language for problem solving, The Practical Application of the Process Capability Study: Evolving from Product Control to Process Control delineates the process capability study, a powerful tool that, when understood and implemented, provides benefits to every department within a manufacturing organization. With easy to read, step-by-step flow diagrams on how to perform process capability studies and measurement process analyses, the book’s coverage includes: The benefits of statistical process control over statistical product control Real-world industrial examples and case studies illustrating how to use the techniques Ways for management to determine if the investment in process capability studies is providing an appropriate return Methods to correct lack of stability and capability once either condition has been identified, such as the ANOVA technique and the simple three-factor designed experiment A flow chart that enables machine operators to execute a process capability study without interfering with productivity A great deal of information is available on the technical concepts of the process capability study, much of it emphasizing the mathematics. Unfortunately, concentrating on the math and fine distinctions, such as the difference between alpha- and beta-type errors, has created barriers preventing many from fully appreciating the basic concepts, the simplicity, and the usefulness of the tool. This book shows you how to use the process capability study to increase return on investment from your statistical process control/Six Sigma effort and make your company more competitive.

Process Capability Analysis: Estimating Quality presents a systematic exploration of process capability analysis and how it may be used to estimate quality. The book is designed for practitioners who are tasked with insuring a high level of quality for the products and services offered by their organizations. Along with describing the necessary statistical theory, the book illustrates the practical application of the techniques to data that do not always satisfy the standard assumptions. The first two chapters deal with attribute data, where the estimation of quality is restricted to counts of nonconformities. Both classical and Bayesian methods are discussed. The rest of the book deals with variable data, including extensive discussions of both capability indices and statistical tolerance limits. Considerable emphasis is placed on methods for handling non-normal data. Also included are discussions of topics often omitted in discussions of process capability, including multivariate capability indices, multivariate tolerance limits, and capability control charts. A separate chapter deals with the problem of determining adequate sample sizes for estimating process capability. Features:  Comprehensive treatment of the subject with consistent theme of estimating percent of nonconforming product or service.  Includes Bayesian methods.  Extension of univariate techniques to multivariate data.  Demonstration of all techniques using Statgraphics data analysis software. Neil Polhemus is Chief Technology Officer at Statgraphics Technology and the original developer of the Statgraphics program for statistical analysis and data visualization. Dr. Polhemus spent 6 years on the faculty of the School of Engineering and Applied Science at Princeton University before moving full-time to software development and consulting. He has taught courses dealing with statistical process control, design of experiments and data analysis for more than 100 companies and government agencies.

Process Capability Analysis: Estimating Quality presents a systematic exploration of process capability analysis and how it may be used to estimate quality. The book is designed for practitioners who are tasked with insuring a high level of quality for the products and services offered by their organizations. Along with describing the necessary statistical theory, the book illustrates the practical application of the techniques to data that do not always satisfy the standard assumptions. The first two chapters deal with attribute data, where the estimation of quality is restricted to counts of nonconformities. Both classical and Bayesian methods are discussed. The rest of the book deals with variable data, including extensive discussions of both capability indices and statistical tolerance limits. Considerable emphasis is placed on methods for handling non-normal data. Also included are discussions of topics often omitted in discussions of process capability, including multivariate capability indices, multivariate tolerance limits, and capability control charts. A separate chapter deals with the problem of determining adequate sample sizes for estimating process capability. Features:  Comprehensive treatment of the subject with consistent theme of estimating percent of nonconforming product or service.  Includes Bayesian methods.  Extension of univariate techniques to multivariate data.  Demonstration of all techniques using Statgraphics data analysis software. Neil Polhemus is Chief Technology Officer at Statgraphics Technology and the original developer of the Statgraphics program for statistical analysis and data visualization. Dr. Polhemus spent 6 years on the faculty of the School of Engineering and Applied Science at Princeton University before moving full-time to software development and consulting. He has taught courses dealing with statistical process control, design of experiments and data analysis for more than 100 companies and government agencies.

This unique volume provides an up-to-date and detailed description of the various process capability indices widely (and sometimes misleadingly) used in the applications at production sites. The authors, who are internationally recognized experts in this area with numerous contributions to the field, provide a lucid exposition, which covers all the main aspects, developments and advances.The concept of Process Capability Index (PCI) is barely 20 years old, but the multitude of available versions can overwhelm even the most seasoned practitioner. The organized and self-contained presentation of the material starting from 1980's primitive indices (Cp and Cpk) up to the newly proposed indices for the cases of multiple dependent characteristics results in an authoritative and indispensable reference.A proper balance between theoretical investigation and “rule-of-thumb” practical procedures is maintained in order to eliminate the tensions among various methodologies of assessing the capability of industrial processes.

A solid, rigorous, yet comprehensible analysis of process capability indices, this work bridges the gap between theoretical statisticians and quality control practitioners, showing how an understanding of these indices can lead to process improvement.

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